What are some benefits of using graphs of frequency distributions?
Organizing a data set into a frequency distribution can make patterns within the data more evident. This is particularly true for quantitative data, which may not be easy to visualize in numerical form.
Bar charts, histograms and line charts are three ways to represent frequency distributions in graphical form. They are appropriate for both numerical and categorical data.
A bar chart is a visual display of frequencies using columns plotted on a graph, usually vertically or horizontally, with the X-axis representing a variable and the Y-axis displaying the frequency count. When smoothed, the chart takes the shape of a curve.
Histograms are another graphical way of representing frequency distributions in which scores (midpoints) are plotted on the X-axis and the frequencies are plotted on the Y-axis. Each score value is plotted in a horizontal bar, with the width of the bar representing the real limits of the interval and the height of the bar corresponding to the frequency of the occurrence of the score value.
Absolute and relative cumulative frequency polygons are two more graphical ways of representing frequency distributions in which points are drawn on the graph at the intersection of the midpoint of each score interval and the height of the frequency. These points are connected with lines, resulting in a polygon that contains all the score values.
An absolute frequency polygon begins with the midpoint of the lowest score interval and ends with the interval immediately higher than the highest score interval. All values too small to fit in the first bin are omitted from the analysis. This type of polygon is especially useful when comparing a large data set to another one that has fewer values than the original data set.